Statistical Learning Workshop

Machine learning can be loosely defined as the set of computational methods that use experience to improve performance or make accurate predictions. With the revival of neural networks and the advent of deep learning, machine learning research now focuses on the development of even more complex models,  requiring very large training sets and huge amounts of computational power. Understanding the properties of these models and analyzing them is, at least for the moment, receiving less attention in machine learning research. To fill this gap, one possibility is to rely on one of the pillars of machine learning: statistics.

Many machine learning algorithms rely on statistical models, such as ridge and lasso regression. The statistics community has derived precise theoretical results of these models, including their asymptotic properties or the construction of confidence intervals for parameters. They enable model interpretation, robust inference and causal statements. Such results are mostly still missing in machine learning world, because the models that are used there are inherently complex.

In this workshop we bring together the research communities of statistics and machine learning to foster a discussion between the two fields and develop research synergies. The workshop takes place on-line the whole day of 18/September. Depending on the COVID-19 status at the time a restricted physical presence version might also take place. The workshop will feature talks by:

In addition, a number of PhD students will present their work in talks. A more detailed program will soon be available on the workshop site. Please register for the workshop by sending an e-mail with subject 'Registration' at in order to provide you with the workshop on-line link and inform you if there will also be the possibility of physical presence.


The organizers, 

Prof. Sebastian Engelke, UNIGE, GSEM

Prof. Alexandros Kalousis, HES-SO

Prof. Davide La Vecchia, UNIGE, GSEM